Peak detection is a necessary process in many two-dimensional images, for example, in nuclear medicine imaging systems, such as single-photon emission computed tomography (SPECT) and positron emission tomography (PET). These systems include gamma detectors, in which pixelated scintillation crystals may be applied. Such pixelated crystals may be optically coupled with an array of photocathodes. Interaction of gamma photons with a scintillator produces light photons. The light photons are then converted to electrons by interacting with the photocathode after they pass through the scintillator crystal. Subsequently, the electrons are converted to electric signals and amplified in front-end electronics in order to be translated to information relating to incidence position of a primary gamma photon to the crystal. Due to noise and errors in detection systems, there is an uncertainty in estimating the incidence position of the gamma photons. Given such uncertainty in positioning, a positioning calibration may be necessary in nuclear medicine imaging modalities. When pixelated crystals are used, a two-dimensional image which includes some hot points (also referred to as peaks), each corresponding to a pixel, is generated by irradiating the crystal with a uniform emission of gamma photons. Such a two-dimensional image may be referred to as a “flood image.” Since the flood-field image may have a low quality and the response of each pixel may be a blurry point in a noisy background, determining the peak position of each blurry response may be needed.
The process of locating the position of these peaks is sometimes referred to as peak detection, and is one of the primary steps in a position calibration procedure. Performance of peak detection has a significant impact on positioning and imaging quality. Various algorithms have been developed for peak detection. However, these algorithms fail to provide appropriate results when noisy images are generated in nuclear medicine imaging. Manual methods are also used in which a user determines the peak position. However, such methods are highly dependent on the user's skill. In addition, they suffer from not being reproducible, high inter-variability, and time consumption when there are many detector blocks to be calibrated.
There is therefore a need for an automatic peak detection algorithm that can provide accurate results in the presence of noise and distortion. There is also a need for a method that accurately maps each detected peak in the peak-detected image to the true position of the corresponding pixel in the original two-dimensional image.